1. Install the requirements

   $ pip install -r requirements.txt
   

2. Run the app

   $ streamlit run /src/App.py
   

FIFA Data Lab is an interactive platform that allows users to explore FIFA data spanning from 2017 to 2022. The app uses machine learning to predict player salaries and market values based on various attributes such as player ratings, age, and skills.

• Data Summary:A brief overview of the datasets used in the project, including key player attributes such as age, position, overall rating, and market value..
• Data Visualization:Graphical representations of the data to help understand the distribution of player attributes, market value, and salary trends.
• Data Prediction:Using linear regression to predict the salaries of football players based on their attributes from the dataset

Our goal is to make FIFA player analysis accessible to everyone, from casual gamers to data science enthusiasts. We aim to help users uncover valuable insights from FIFA data through an easy-to-use, interactive app.

We would like to thank EASports, the creators of the FIFA games, for providing such detailed datasets and for entertaining millions of people around the world. A special thanks to Brayan from Kaggle and the open-source community for the tools and libraries (like Pandas, Scikit-learn, Seaborn, and Streamlit) that helped make this app possible. We also appreciate all the FIFA fans and data enthusiasts who contributed. Lastly, we want to thank Mr Gaetan Breson , our instructor for Data Science for Everyone at NYU Paris, who inspired us to create this project.